Guzmán, Jose APinto-Ledezma, Jesús NFrantz, DavidTownsend, Philip AJuzwik, JenniferCavender-Bares, Jeannine2023-09-132023-09-132023-12-01https://hdl.handle.net/11299/256901The data for this article is openly available from different sources. The spatial points and polygons to extract LSP pixels as well as the coregistered high-resolution RGB images are available at DRYAD (https://doi.org/10.5061/dryad.gtht76hs8). The code for processing the satellite data is available through GitHub (https://github.com/davidfrantz/force), while the code for the z-score normalization of scenes and the development and prediction of the machine learning models is available also at GitHub (https://github.com/ASCEND-BII/Oak-wilt.git) and archived at Zenodo under version 1.0 (https://doi.org/10.5281/zenodo.8275122). The LSP metrics computed across states are available at https://app.globus.org/file-manager?origin_id=d5f9b461-7d6e-442b-87ed-be8aa2ca6763&origin_path=%2F, while the predicted maps at https://app.globus.org/file-manager?origin_id=2ad70821-cc5a-424e-aa72-8553d2bb45eb&origin_path=%2FProtecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt —a disease caused by a pathogenic fungus (Bretziella fagacearum)— is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80 and 82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can identify healthy (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders to locate disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and to monitor large-scale change in biodiversity.enAnalysis-ready dataQuercusPlant pathogensTime seriesLarge-area mappingBig dataPhenologyArticlehttps://doi.org/10.1016/j.rse.2023.113794